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Distinct characteristics of correlation analysis at the single-cell and the population level

  • Guoyu Wu ORCID logo EMAIL logo and Yuchao Li
Published/Copyright: August 2, 2022
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 21 Issue 1

Abstract

Correlation analysis is widely used in biological studies to infer molecular relationships within biological networks. Recently, single-cell analysis has drawn tremendous interests, for its ability to obtain high-resolution molecular phenotypes. It turns out that there is little overlap of co-expressed genes identified in single-cell level investigations with that of population level investigations. However, the nature of the relationship of correlations between single-cell and population levels remains unclear. In this manuscript, we aimed to unveil the origin of the differences between the correlation coefficients at the single-cell level and that at the population level, and bridge the gap between them. Through developing formulations to link correlations at the single-cell and the population level, we illustrated that aggregated correlations could be stronger, weaker or equal to the corresponding individual correlations, depending on the variations and the correlations within the population. When the correlation within the population is weaker than the individual correlation, the aggregated correlation is stronger than the corresponding individual correlation. Besides, our data indicated that aggregated correlation is more likely to be stronger than the corresponding individual correlation, and it was rare to find gene-pairs exclusively strongly correlated at the single-cell level. Through a bottom-up approach to model interactions between molecules in a signaling cascade or a multi-regulator-controlled gene expression, we surprisingly found that the existence of interaction between two components could not be excluded simply based on their low correlation coefficients, suggesting a reconsideration of connectivity within biological networks which was derived solely from correlation analysis. We also investigated the impact of technical random measurement errors on the correlation coefficients for the single-cell level and the population level. The results indicate that the aggregated correlation is relatively robust and less affected. Because of the heterogeneity among single cells, correlation coefficients calculated based on data of the single-cell level might be different from that of the population level. Depending on the specific question we are asking, proper sampling and normalization procedure should be done before we draw any conclusions.

Keywords: correlation analysis; mathematical modeling; measurement errors; population level; single-cell level
Corresponding author: Guoyu Wu, School of Clinical Pharmacy, Guangdong Pharmaceutical University, Guangzhou, China; Key Specialty of Clinical Pharmacy, School of Clinical Pharmacy, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China; and NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangdong Pharmaceutical University, Guangzhou, China, E-mail:

Guoyu Wu and Yuchao Li are equally contributed

Funding source: Scientific research funding of the First Affiliated Hospital of Guangdong Pharmaceutical University

Award Identifier / Grant number: KYQDJF202016

Funding source: National Key Clinical Specialty Construction Project (Clinical Pharmacy) and High Level Clinical Key Specialty (Clinical Pharmacy) in Guangdong Province

Funding source: The Construction Project of NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance

Funding source: Medical Scientific Research Foundation of Guangdong Province, China

Award Identifier / Grant number: A2022182

Funding source: Cultivation Fund of National Natural Science Foundation of China，School of Clinical Pharmacy, Guangdong Pharmaceutical University

Award Identifier / Grant number: SCP2022-07

  1. Author contribution: GW: Conceptualization, implementation, investigation, writing, editing and revising the manuscript. YL: Investigation, editing and revising the manuscript. All authors read and approved the final manuscript.

  2. Research funding: This study was supported by Medical Scientific Research Foundation of Guangdong Province of China (A2022182). This study was also supported by Scientific research funding of the First Affiliated Hospital of Guangdong Pharmaceutical University (KYQDJF202016). This study was also supported by National Key Clinical Specialty Construction Project (Clinical Pharmacy) and High Level Clinical Key Specialty (Clinical Pharmacy) in Guangdong Province. This study was also supported by the Construction Project of NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance. This study was also supported by Cultivation Fund of National Natural Science Foundation of China, School of Clinical Pharmacy, Guangdong Pharmaceutical University (SCP2022-07).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article conflict of interest exits in the submission of this manuscript.

  4. Data accessibility statement: The data that support the findings of this study are available from the corresponding author upon reasonable request

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/sagmb-2022-0015).

Received: 2022-03-27
Revised: 2022-05-16
Accepted: 2022-06-13
Published Online: 2022-08-02

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/sagmb-2022-0015
Keywords for this article
correlation analysis; mathematical modeling; measurement errors; population level; single-cell level

Articles in the same Issue

  1. Review Article
  2. Challenges for machine learning in RNA-protein interaction prediction
  3. Research Articles
  4. Distinct characteristics of correlation analysis at the single-cell and the population level
  5. pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples
  6. Use of SVM-based ensemble feature selection method for gene expression data analysis
  7. A robust association test with multiple genetic variants and covariates
  8. Estimation of the covariance structure from SNP allele frequencies
  9. GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing
  10. Sparse latent factor regression models for genome-wide and epigenome-wide association studies
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